class_docstr: Binomial variance function
  
  p = mu / n; V(mu) = p * (1 - p) * n
  
  INPUTS:
     n -- number of trials in Binomial
docstr: Binomial variance function
  
  INPUTS:
     mu  -- mean parameters
  
  OUTPUTS: v
     v   -- mu / self.n * (1 - mu / self.n) * self.n
docstr_tok: ["Binomial","variance","function","INPUTS",":","mu","--","mean","parameters","OUTPUTS",":","v","v","--","mu","\/","self.n","*","(","1","-","mu","\/","self.n",")","*","self.n"]
code: def __call__(self, mu):
      """
      Binomial variance function
  
      INPUTS:
         mu  -- mean parameters
  
      OUTPUTS: v
         v   -- mu / self.n * (1 - mu / self.n) * self.n
      """
      p = self.clean(mu / self.n)
      return p * (1 - p) * self.n
  
file_path: nipy/nipy/algorithms/statistics/models/family/varfuncs.py
imports: import numpy
label_desc: Use image node_id 3 for calling the Binomial obj's underlying member method code with example usage: obj.__call__(mu) and returns: unknown